no code implementations • 28 Aug 2023 • Sahil Verma, Ashudeep Singh, Varich Boonsanong, John P. Dickerson, Chirag Shah
To the best of our knowledge, this work is the first to conceptualize and empirically test a generalized framework for generating recourses for recommender systems.
no code implementations • 24 May 2023 • Pedro Silva, Bhawna Juneja, Shloka Desai, Ashudeep Singh, Nadia Fawaz
To improve representation in search results and recommendations, we introduce end-to-end diversification, ensuring that diverse content flows throughout the various stages of these systems, from retrieval to ranking.
1 code implementation • NeurIPS 2021 • Ashudeep Singh, David Kempe, Thorsten Joachims
We call an algorithm $\phi$-fair (for $\phi \in [0, 1]$) if it has the following property for all agents $x$ and all $k$: if agent $x$ is among the top $k$ agents with respect to merit with probability at least $\rho$ (according to the posterior merit distribution), then the algorithm places the agent among the top $k$ agents in its ranking with probability at least $\phi \rho$.
1 code implementation • 29 May 2020 • Marco Morik, Ashudeep Singh, Jessica Hong, Thorsten Joachims
Rankings are the primary interface through which many online platforms match users to items (e. g. news, products, music, video).
1 code implementation • NeurIPS 2019 • Ashudeep Singh, Thorsten Joachims
Conventional Learning-to-Rank (LTR) methods optimize the utility of the rankings to the users, but they are oblivious to their impact on the ranked items.
no code implementations • 20 Feb 2018 • Ashudeep Singh, Thorsten Joachims
Rankings are ubiquitous in the online world today.
no code implementations • 17 Feb 2016 • Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims
Most data for evaluating and training recommender systems is subject to selection biases, either through self-selection by the users or through the actions of the recommendation system itself.
no code implementations • 4 Jun 2014 • Divyanshu Bhartiya, Ashudeep Singh
Sentence extraction based summarization methods has some limitations as it doesn't go into the semantics of the document.